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Deep learning-based real-time VPN encrypted traffic identification methods

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Abstract

With the widespread application of virtual private network (VPN) technology, real-time VPN traffic identification has become an increasingly important task in network management and security maintenance. Since traditional encrypted traffic identification technology is not effective in feature extraction and selection, this paper proposes two deep learning-based models to classify the traffic into VPN and non-VPN traffic, identify VPN traffic generated by six different applications much further. Our models utilize convolutional auto-encoding (CAE) and convolutional neural network (CNN), respectively, preprocessing the traffic samples into session pictures, to accomplish the experiment objectives. The CAE-based method, utilizing the unsupervised nature of CAE to extract the hidden layer features, can automatically learn the nonlinear relationship between original input and expected output. The CNN-based method performs well in extracting two-dimensional local features of images. Experimental results show that our models perform better than traditional identification methods. In the two-category identification, the best result comes from the CAE-based model; the overall identification accuracy rate is 98.77%. Among the six-category identification, the best result comes from CNN-based model; the overall identification accuracy rate is 92.92%.

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Authors and Affiliations

  1. Cyberspace Security Institute, Information Engineering University, Zhengzhou, China

    Lulu Guo, Shengli Liu, Ming Duan, Huijie Li & Jianwen Sun

  2. Institute of Electrical Engineering and Automation, Luoyang Institute of Science and Technology, Luoyang, China

    Qianqiong Wu

  3. Tampere University of Applied Sciences, Pirkanmaa, Finland

    Qianqiong Wu

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  1. Lulu Guo
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  2. Qianqiong Wu
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  3. Shengli Liu
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  4. Ming Duan
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  5. Huijie Li
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  6. Jianwen Sun
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Correspondence to Lulu Guo.

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Guo, L., Wu, Q., Liu, S. et al. Deep learning-based real-time VPN encrypted traffic identification methods. J Real-Time Image Proc 17, 103–114 (2020). https://doi.org/10.1007/s11554-019-00930-6

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